I’m trying to compile the latest version of Tesseract OCR on AWS SageMaker (Amazon Linux 2), so I can use PyMupdf OCR functions. After successfully installing Leptonica 1.85.0 from source, I attempted ...

I am trying to train a neural network (Tensorflow) on AWS. I have some AWS credits. From my understanding AWS SageMaker is the one best for the job. I managed to load the Jupyter Lab console on Sag...

2019年3月13日 · SageMaker is better for Deployment. and if you are not working on big data, SageMaker is a perfect choice working with (Jupyter notebook + Sklearn + Mature containers + Super easy deployment). SageMaker provides "real time inference", very easy to build and deploy, very impressive. you can check the official SageMaker Github.

2020年7月28日 · There is no way to have your local mode training jobs appear in the AWS console. The intent of local mode is to allow for faster iteration/debugging before using SageMaker for training your model.

2024年2月21日 · SageMaker uses /PING and /Invocations to respond, the fact that you are seeing 500 errors on /Ping tells me that you might not be reaching the container successfully. To debug this issue, I would advise you to 1/ Try local Inference within your SageMaker notebook instance( or ec2) to run the container and check for any issues. 2/ Add debug logs ...

2024年1月22日 · Like a few other managed services, SageMaker might "run on EC2" in the sense of using the same underlying compute platform and (a subset of) matching instance types - but the instances it creates are fully-managed by the service: You won't see them in your account's EC2 console, and you can't use your existing EC2 instances and SageMaker ...

2018年3月19日 · Amazon Sagemaker is built to help you scale your training activities. With large datasets, you might consider two main aspects: The way data are stored and accessed, The actual training parallelism. Data storage: S3 is the most cost-effective way to store your data for training. To get faster startup and training times, you can consider the ...

2021年12月9日 · I had the same issue. With this line, you can install the glib dependency for Amazon Sagemaker Studio Lab. Just run it on your notebook cell.! conda install glib=2.51.0 -y You also can create another virtual environment for your session that contains glib:! conda create -n glib-test -c defaults -c conda-forge python=3 glib=2.51.0` -y

2024年1月13日 · amazon-sagemaker; Share. Improve this question. Follow edited Feb 2, 2024 at 16:11. Majid Kamyab . asked ...

2018年7月12日 · On the side note, you can also use EFS file system with SageMaker to have more space with Notebook Instance. There is blog on how to add an EFS mount with Lifecycle actions, here Mount an EFS file system to an Amazon SageMaker notebook (with lifecycle configurations). Let us know if there is any other way we can be of assistance.